Technologies for classification and clustering Clause Samples

Technologies for classification and clustering. Several frameworks and libraries are already available for classification and clustering. In the following, we introduce the most famous ones: 16 ▇▇▇▇://▇▇▇▇▇▇.▇▇▇▇▇▇.▇▇▇/ 18 ▇▇▇▇▇://▇▇▇.▇▇▇▇▇▇.▇▇▇/ 19 ▇▇▇▇▇://▇▇▇▇▇▇.▇▇▇/lyst/lightfm 20 ▇▇▇▇://▇▇▇.▇▇▇▇▇▇▇▇▇▇▇.▇▇▇ 21 ▇▇▇▇▇://▇▇▇▇▇▇.▇▇▇/RankSys/RankSys ● Apache Mahout22 (Apache License v2) includes a Java implementation for popular machine learning algorithms for classification, clustering and topic modelling. The library is highly scalable, and it is characterized by having a good documentation, high performance, and maintainability. ● Scikit-learn23 (BSD license) offers a Python implementation of the most popular machine learning algorithms for classification and clustering. The library is characterized by an excellent documentation, high performance, ease of use and maintainability. The drawbacks are scalability and heavy multiprocessing instead of lightweight multithreading (due to Python GIL limitations - see ▇▇▇▇▇://▇▇▇▇.▇▇▇▇▇▇.▇▇▇/moin/GlobalInterpreterLock). ● Weka24 (GNU General Public License), developed in Java, supports diverse machine learning approaches for classification, and clustering and attribute selection. In addition to the graphical user interface provided, it can be accessed via a Java API.
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  • Formation and Composition The Parties to this agreement will maintain a Joint Administration and Dispute Resolution Committee (JADRC) consisting of five (5) representatives of the employers and five (5) representatives of the Provincial Bargaining Council.

  • RECOGNITION AND MEMBERSHIP 16 Section 1. The Hospital recognizes the Association as the collective bargaining 17 representative with respect to wage rates, hours of work, and other conditions of 18 employment for a bargaining unit composed of all categories of registered nurses 20 Hospital shall not challenge the status of bargaining unit nurses or assert that 21 bargaining unit nurses are supervisors.

  • DIVERSIFICATION AND RELATED LIMITATIONS 6.1. The Trust and MFS represent and warrant that each Portfolio of the Trust will meet the diversification requirements of Section 817 (h) (1) of the Code and Treas. Reg. 1.817-5, relating to the diversification requirements for variable annuity, endowment, or life insurance contracts, as they may be amended from time to time (and any revenue rulings, revenue procedures, notices, and other published announcements of the Internal Revenue Service interpreting these sections), as if those requirements applied directly to each such Portfolio. 6.2. The Trust and MFS represent that each Portfolio will elect to be qualified as a Regulated Investment Company under Subchapter M of the Code and that they will maintain such qualification (under Subchapter M or any successor or similar provision).

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